Overview

Brought to you by YData

Dataset statistics

Number of variables50
Number of observations180862
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.0 MiB
Average record size in memory400.0 B

Variable types

Numeric10
Categorical34
Text6

Alerts

PUFSVYMO has constant value "4" Constant
PUFSVYYR has constant value "2016" Constant
PUFC01_LNO is highly overall correlated with PUFC03_REL and 1 other fieldsHigh correlation
PUFC03_REL is highly overall correlated with PUFC01_LNO and 1 other fieldsHigh correlation
PUFC05_AGE is highly overall correlated with PUFC01_LNO and 6 other fieldsHigh correlation
PUFC06_MSTAT is highly overall correlated with PUFC08_CURSCH and 1 other fieldsHigh correlation
PUFC08_CURSCH is highly overall correlated with PUFC05_AGE and 1 other fieldsHigh correlation
PUFC09_GRADTECH is highly overall correlated with PUFC05_AGE and 3 other fieldsHigh correlation
PUFC10_CONWR is highly overall correlated with PUFC09_GRADTECH and 3 other fieldsHigh correlation
PUFC11_WORK is highly overall correlated with PUFC05_AGE and 13 other fieldsHigh correlation
PUFC12_JOB is highly overall correlated with PUFC11_WORK and 10 other fieldsHigh correlation
PUFC14_PROCC is highly overall correlated with PUFC11_WORK and 9 other fieldsHigh correlation
PUFC17_NATEM is highly overall correlated with PUFC11_WORK and 10 other fieldsHigh correlation
PUFC18_PNWHRS is highly overall correlated with PUFC11_WORK and 8 other fieldsHigh correlation
PUFC20_PWMORE is highly overall correlated with PUFC11_WORK and 10 other fieldsHigh correlation
PUFC21_PLADDW is highly overall correlated with PUFC11_WORK and 10 other fieldsHigh correlation
PUFC22_PFWRK is highly overall correlated with PUFC11_WORK and 10 other fieldsHigh correlation
PUFC23_PCLASS is highly overall correlated with PUFC11_WORK and 9 other fieldsHigh correlation
PUFC26_OJOB is highly overall correlated with PUFC11_WORK and 11 other fieldsHigh correlation
PUFC27_NJOBS is highly overall correlated with PUFC26_OJOBHigh correlation
PUFC30_LOOKW is highly overall correlated with PUFC31_FLWRK and 6 other fieldsHigh correlation
PUFC31_FLWRK is highly overall correlated with PUFC30_LOOKW and 2 other fieldsHigh correlation
PUFC32_JOBSM is highly overall correlated with PUFC30_LOOKW and 1 other fieldsHigh correlation
PUFC33_WEEKS is highly overall correlated with PUFC30_LOOKW and 1 other fieldsHigh correlation
PUFC34_WYNOT is highly overall correlated with PUFC30_LOOKW and 6 other fieldsHigh correlation
PUFC35_LTLOOKW is highly overall correlated with PUFC34_WYNOTHigh correlation
PUFC36_AVAIL is highly overall correlated with PUFC34_WYNOT and 2 other fieldsHigh correlation
PUFC37_WILLING is highly overall correlated with PUFC34_WYNOT and 2 other fieldsHigh correlation
PUFC38_PREVJOB is highly overall correlated with PUFC30_LOOKW and 4 other fieldsHigh correlation
PUFC40_POCC is highly overall correlated with PUFC30_LOOKW and 2 other fieldsHigh correlation
PUFC41_WQTR is highly overall correlated with PUFC05_AGE and 16 other fieldsHigh correlation
PUFHHNUM is highly overall correlated with PUFREG and 1 other fieldsHigh correlation
PUFNEWEMPSTAT is highly overall correlated with PUFC05_AGE and 18 other fieldsHigh correlation
PUFPRRCD is highly overall correlated with PUFPRVHigh correlation
PUFPRV is highly overall correlated with PUFPRRCDHigh correlation
PUFPSU is highly overall correlated with PUFPWGTFINHigh correlation
PUFPWGTFIN is highly overall correlated with PUFPSUHigh correlation
PUFREG is highly overall correlated with PUFHHNUM and 1 other fieldsHigh correlation
PUFURB2K10 is highly overall correlated with PUFHHNUM and 1 other fieldsHigh correlation
PUFC10_CONWR is highly imbalanced (59.7%) Imbalance
PUFC18_PNWHRS is highly imbalanced (50.1%) Imbalance
PUFC24_PBASIS is highly imbalanced (62.1%) Imbalance
PUFC27_NJOBS is highly imbalanced (91.3%) Imbalance
PUFC29_WWM48H is highly imbalanced (78.4%) Imbalance
PUFC31_FLWRK is highly imbalanced (93.2%) Imbalance
PUFC32_JOBSM is highly imbalanced (95.5%) Imbalance
PUFC33_WEEKS is highly imbalanced (97.4%) Imbalance
PUFC34_WYNOT is highly imbalanced (59.7%) Imbalance
PUFC35_LTLOOKW is highly imbalanced (95.7%) Imbalance
PUFC36_AVAIL is highly imbalanced (85.0%) Imbalance
PUFC37_WILLING is highly imbalanced (85.2%) Imbalance
PUFC40_POCC is highly imbalanced (76.9%) Imbalance
PUFC05_AGE has 3214 (1.8%) zeros Zeros

Reproduction

Analysis started2025-03-23 14:45:37.090815
Analysis finished2025-03-23 14:46:14.218702
Duration37.13 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

PUFREG
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3988013
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:14.630980image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q313
95-th percentile16
Maximum17
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6670343
Coefficient of variation (CV)0.49655633
Kurtosis-1.1864282
Mean9.3988013
Median Absolute Deviation (MAD)4
Skewness-0.12608623
Sum1699886
Variance21.781209
MonotonicityIncreasing
2025-03-23T22:46:14.725861image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13 25646
14.2%
3 13965
 
7.7%
6 12211
 
6.8%
8 12092
 
6.7%
10 10755
 
5.9%
14 10390
 
5.7%
5 10318
 
5.7%
7 10225
 
5.7%
15 9778
 
5.4%
16 9417
 
5.2%
Other values (7) 56065
31.0%
ValueCountFrequency (%)
1 6707
3.7%
2 6321
3.5%
3 13965
7.7%
4 8567
4.7%
5 10318
5.7%
6 12211
6.8%
7 10225
5.7%
8 12092
6.7%
9 7188
4.0%
10 10755
5.9%
ValueCountFrequency (%)
17 8918
 
4.9%
16 9417
 
5.2%
15 9778
 
5.4%
14 10390
5.7%
13 25646
14.2%
12 9155
 
5.1%
11 9209
 
5.1%
10 10755
5.9%
9 7188
 
4.0%
8 12092
6.7%

PUFPRV
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.825309
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:14.842546image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q124
median46
Q371
95-th percentile80
Maximum98
Range97
Interquartile range (IQR)47

Descriptive statistics

Standard deviation24.939767
Coefficient of variation (CV)0.54423567
Kurtosis-1.174851
Mean45.825309
Median Absolute Deviation (MAD)23
Skewness-0.10163831
Sum8288057
Variance621.992
MonotonicityNot monotonic
2025-03-23T22:46:14.967191image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 10167
 
5.6%
75 7550
 
4.2%
74 6215
 
3.4%
22 6186
 
3.4%
73 3618
 
2.0%
2 3579
 
2.0%
37 3440
 
1.9%
35 3369
 
1.9%
43 3326
 
1.8%
30 3252
 
1.8%
Other values (76) 130160
72.0%
ValueCountFrequency (%)
1 1613
0.9%
2 3579
2.0%
3 1743
1.0%
4 1739
1.0%
5 1743
1.0%
6 1611
0.9%
7 1729
1.0%
8 1750
1.0%
10 1504
0.8%
11 3116
1.7%
ValueCountFrequency (%)
98 1042
0.6%
97 745
0.4%
86 1551
0.9%
85 755
0.4%
83 1257
0.7%
82 1568
0.9%
81 782
0.4%
80 1697
0.9%
79 763
0.4%
78 1704
0.9%

PUFPRRCD
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4585.0553
Minimum100
Maximum9804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:15.094038image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile500
Q12402
median4600
Q37100
95-th percentile8000
Maximum9804
Range9704
Interquartile range (IQR)4698

Descriptive statistics

Standard deviation2494.0287
Coefficient of variation (CV)0.54394736
Kurtosis-1.1736458
Mean4585.0553
Median Absolute Deviation (MAD)2300
Skewness-0.102321
Sum8.2926228 × 108
Variance6220179.3
MonotonicityNot monotonic
2025-03-23T22:46:15.227168image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3600 2639
 
1.5%
6600 2282
 
1.3%
7501 2106
 
1.2%
7503 1985
 
1.1%
4800 1899
 
1.0%
3747 1866
 
1.0%
7332 1836
 
1.0%
202 1830
 
1.0%
5500 1810
 
1.0%
2000 1792
 
1.0%
Other values (106) 160817
88.9%
ValueCountFrequency (%)
100 1613
0.9%
200 1749
1.0%
202 1830
1.0%
300 1743
1.0%
400 1739
1.0%
500 1743
1.0%
600 1611
0.9%
700 1729
1.0%
800 1750
1.0%
1000 1504
0.8%
ValueCountFrequency (%)
9804 1042
0.6%
9700 745
0.4%
8600 1551
0.9%
8500 755
0.4%
8300 1257
0.7%
8200 1568
0.9%
8100 782
0.4%
8000 1697
0.9%
7900 763
0.4%
7800 1704
0.9%

PUFHHNUM
Real number (ℝ)

High correlation 

Distinct40880
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20528.232
Minimum1
Maximum40880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:15.421943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2052
Q110256.25
median20406
Q330962
95-th percentile38688
Maximum40880
Range40879
Interquartile range (IQR)20705.75

Descriptive statistics

Standard deviation11827.708
Coefficient of variation (CV)0.57616789
Kurtosis-1.2180446
Mean20528.232
Median Absolute Deviation (MAD)10353
Skewness-0.0072181704
Sum3.7127771 × 109
Variance1.3989468 × 108
MonotonicityIncreasing
2025-03-23T22:46:15.552117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3654 23
 
< 0.1%
27675 19
 
< 0.1%
31859 18
 
< 0.1%
5542 18
 
< 0.1%
1317 18
 
< 0.1%
36833 17
 
< 0.1%
5591 17
 
< 0.1%
24738 17
 
< 0.1%
6526 17
 
< 0.1%
18095 17
 
< 0.1%
Other values (40870) 180681
99.9%
ValueCountFrequency (%)
1 3
< 0.1%
2 4
< 0.1%
3 4
< 0.1%
4 4
< 0.1%
5 3
< 0.1%
6 5
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 4
< 0.1%
10 6
< 0.1%
ValueCountFrequency (%)
40880 5
 
< 0.1%
40879 7
< 0.1%
40878 6
< 0.1%
40877 4
 
< 0.1%
40876 4
 
< 0.1%
40875 9
< 0.1%
40874 1
 
< 0.1%
40873 10
< 0.1%
40872 13
< 0.1%
40871 2
 
< 0.1%

PUFURB2K10
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2
103986 
1
76876 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 103986
57.5%
1 76876
42.5%

Length

2025-03-23T22:46:15.668890image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:15.753815image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 103986
57.5%
1 76876
42.5%

Most occurring characters

ValueCountFrequency (%)
2 103986
57.5%
1 76876
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 103986
57.5%
1 76876
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 103986
57.5%
1 76876
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 103986
57.5%
1 76876
42.5%

PUFPWGTFIN
Real number (ℝ)

High correlation 

Distinct35599
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean568.52717
Minimum34.9984
Maximum4509.316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:15.856411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum34.9984
5-th percentile122.013
Q1245.06597
median392.9935
Q3679.52677
95-th percentile1699.3154
Maximum4509.316
Range4474.3176
Interquartile range (IQR)434.4608

Descriptive statistics

Standard deviation508.51933
Coefficient of variation (CV)0.89445036
Kurtosis4.851547
Mean568.52717
Median Absolute Deviation (MAD)187.2103
Skewness2.0691578
Sum1.0282496 × 108
Variance258591.91
MonotonicityNot monotonic
2025-03-23T22:46:15.973559image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
710.9543 83
 
< 0.1%
498.4434 73
 
< 0.1%
496.5704 63
 
< 0.1%
289.6611 62
 
< 0.1%
860.0033 61
 
< 0.1%
639.7954 58
 
< 0.1%
290.7537 53
 
< 0.1%
689.2445 51
 
< 0.1%
328.9056 50
 
< 0.1%
388.8613 49
 
< 0.1%
Other values (35589) 180259
99.7%
ValueCountFrequency (%)
34.9984 2
 
< 0.1%
36.8235 2
 
< 0.1%
40.1301 4
 
< 0.1%
48.1864 1
 
< 0.1%
50.3487 5
< 0.1%
51.1338 3
 
< 0.1%
52.9742 2
 
< 0.1%
53.0442 1
 
< 0.1%
53.139 1
 
< 0.1%
53.4283 12
< 0.1%
ValueCountFrequency (%)
4509.316 1
 
< 0.1%
4479.625 1
 
< 0.1%
4386.3567 4
< 0.1%
4364.2543 2
< 0.1%
4300.2437 3
< 0.1%
4258.7984 1
 
< 0.1%
4242.7031 1
 
< 0.1%
4197.0767 1
 
< 0.1%
4164.1584 1
 
< 0.1%
4056.0607 1
 
< 0.1%

PUFSVYMO
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
4
180862 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 180862
100.0%

Length

2025-03-23T22:46:16.081639image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:16.164294image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 180862
100.0%

Most occurring characters

ValueCountFrequency (%)
4 180862
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 180862
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 180862
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 180862
100.0%

PUFSVYYR
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2016
180862 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters723448
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 180862
100.0%

Length

2025-03-23T22:46:16.250399image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:16.332303image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 180862
100.0%

Most occurring characters

ValueCountFrequency (%)
2 180862
25.0%
0 180862
25.0%
1 180862
25.0%
6 180862
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 723448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 180862
25.0%
0 180862
25.0%
1 180862
25.0%
6 180862
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 723448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 180862
25.0%
0 180862
25.0%
1 180862
25.0%
6 180862
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 723448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 180862
25.0%
0 180862
25.0%
1 180862
25.0%
6 180862
25.0%

PUFPSU
Real number (ℝ)

High correlation 

Distinct975
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.27027
Minimum1
Maximum3053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:16.424956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q1107
median243
Q3482
95-th percentile1352
Maximum3053
Range3052
Interquartile range (IQR)375

Descriptive statistics

Standard deviation440.16004
Coefficient of variation (CV)1.1395131
Kurtosis6.5956258
Mean386.27027
Median Absolute Deviation (MAD)165
Skewness2.3737588
Sum69861614
Variance193740.86
MonotonicityNot monotonic
2025-03-23T22:46:16.544385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258 892
 
0.5%
141 794
 
0.4%
111 783
 
0.4%
27 762
 
0.4%
28 741
 
0.4%
54 730
 
0.4%
171 712
 
0.4%
3 706
 
0.4%
58 703
 
0.4%
43 691
 
0.4%
Other values (965) 173348
95.8%
ValueCountFrequency (%)
1 209
 
0.1%
2 368
0.2%
3 706
0.4%
4 627
0.3%
5 312
0.2%
6 306
0.2%
7 656
0.4%
8 454
0.3%
9 484
0.3%
10 264
 
0.1%
ValueCountFrequency (%)
3053 25
 
< 0.1%
2820 91
0.1%
2700 61
< 0.1%
2681 22
 
< 0.1%
2614 32
 
< 0.1%
2584 83
< 0.1%
2578 86
< 0.1%
2564 24
 
< 0.1%
2557 75
< 0.1%
2553 66
< 0.1%

PUFRPL
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
45838 
2
45285 
3
44949 
4
44790 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 45838
25.3%
2 45285
25.0%
3 44949
24.9%
4 44790
24.8%

Length

2025-03-23T22:46:16.655948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:16.746998image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 45838
25.3%
2 45285
25.0%
3 44949
24.9%
4 44790
24.8%

Most occurring characters

ValueCountFrequency (%)
1 45838
25.3%
2 45285
25.0%
3 44949
24.9%
4 44790
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 45838
25.3%
2 45285
25.0%
3 44949
24.9%
4 44790
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 45838
25.3%
2 45285
25.0%
3 44949
24.9%
4 44790
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 45838
25.3%
2 45285
25.0%
3 44949
24.9%
4 44790
24.8%

PUFHHSIZE
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5047827
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:16.842909image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q37
95-th percentile10
Maximum23
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3701694
Coefficient of variation (CV)0.43056548
Kurtosis1.8816138
Mean5.5047827
Median Absolute Deviation (MAD)1
Skewness0.94665033
Sum995606
Variance5.6177029
MonotonicityNot monotonic
2025-03-23T22:46:16.940612image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5 33940
18.8%
4 33852
18.7%
6 27732
15.3%
3 21006
11.6%
7 20195
11.2%
8 13432
 
7.4%
2 9304
 
5.1%
9 7785
 
4.3%
10 4780
 
2.6%
1 2965
 
1.6%
Other values (10) 5871
 
3.2%
ValueCountFrequency (%)
1 2965
 
1.6%
2 9304
 
5.1%
3 21006
11.6%
4 33852
18.7%
5 33940
18.8%
6 27732
15.3%
7 20195
11.2%
8 13432
 
7.4%
9 7785
 
4.3%
10 4780
 
2.6%
ValueCountFrequency (%)
23 23
 
< 0.1%
19 19
 
< 0.1%
18 54
 
< 0.1%
17 119
 
0.1%
16 96
 
0.1%
15 375
 
0.2%
14 434
 
0.2%
13 1027
0.6%
12 1392
0.8%
11 2332
1.3%

PUFC01_LNO
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2523913
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:17.036567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum23
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0771295
Coefficient of variation (CV)0.63864687
Kurtosis2.1025976
Mean3.2523913
Median Absolute Deviation (MAD)1
Skewness1.2143093
Sum588234
Variance4.3144671
MonotonicityNot monotonic
2025-03-23T22:46:17.133456image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 40880
22.6%
2 37915
21.0%
3 33263
18.4%
4 26261
14.5%
5 17798
9.8%
6 11010
 
6.1%
7 6388
 
3.5%
8 3503
 
1.9%
9 1824
 
1.0%
10 959
 
0.5%
Other values (13) 1061
 
0.6%
ValueCountFrequency (%)
1 40880
22.6%
2 37915
21.0%
3 33263
18.4%
4 26261
14.5%
5 17798
9.8%
6 11010
 
6.1%
7 6388
 
3.5%
8 3503
 
1.9%
9 1824
 
1.0%
10 959
 
0.5%
ValueCountFrequency (%)
23 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
< 0.1%
18 5
 
< 0.1%
17 12
 
< 0.1%
16 18
 
< 0.1%
15 43
< 0.1%
14 74
< 0.1%

PUFC03_REL
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9314892
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:17.222085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile7
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8322988
Coefficient of variation (CV)0.62504028
Kurtosis3.0582577
Mean2.9314892
Median Absolute Deviation (MAD)1
Skewness1.6046605
Sum530195
Variance3.3573191
MonotonicityNot monotonic
2025-03-23T22:46:17.311220image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 81297
44.9%
1 40880
22.6%
2 30065
 
16.6%
6 13257
 
7.3%
8 6083
 
3.4%
5 3611
 
2.0%
4 2292
 
1.3%
7 1615
 
0.9%
11 975
 
0.5%
10 752
 
0.4%
ValueCountFrequency (%)
1 40880
22.6%
2 30065
 
16.6%
3 81297
44.9%
4 2292
 
1.3%
5 3611
 
2.0%
6 13257
 
7.3%
7 1615
 
0.9%
8 6083
 
3.4%
9 35
 
< 0.1%
10 752
 
0.4%
ValueCountFrequency (%)
11 975
 
0.5%
10 752
 
0.4%
9 35
 
< 0.1%
8 6083
 
3.4%
7 1615
 
0.9%
6 13257
 
7.3%
5 3611
 
2.0%
4 2292
 
1.3%
3 81297
44.9%
2 30065
 
16.6%

PUFC04_SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
91539 
2
89323 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 91539
50.6%
2 89323
49.4%

Length

2025-03-23T22:46:17.407669image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:17.496790image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 91539
50.6%
2 89323
49.4%

Most occurring characters

ValueCountFrequency (%)
1 91539
50.6%
2 89323
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 91539
50.6%
2 89323
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 91539
50.6%
2 89323
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 91539
50.6%
2 89323
49.4%

PUFC05_AGE
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.889772
Minimum0
Maximum99
Zeros3214
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:17.599850image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median24
Q342
95-th percentile65
Maximum99
Range99
Interquartile range (IQR)31

Descriptive statistics

Standard deviation20.052132
Coefficient of variation (CV)0.71897799
Kurtosis-0.37704568
Mean27.889772
Median Absolute Deviation (MAD)15
Skewness0.65083026
Sum5044200
Variance402.08802
MonotonicityNot monotonic
2025-03-23T22:46:17.720731image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 4141
 
2.3%
10 4076
 
2.3%
7 4044
 
2.2%
12 4033
 
2.2%
5 4012
 
2.2%
4 4002
 
2.2%
9 3986
 
2.2%
13 3974
 
2.2%
15 3947
 
2.2%
3 3877
 
2.1%
Other values (90) 140770
77.8%
ValueCountFrequency (%)
0 3214
1.8%
1 3518
1.9%
2 3728
2.1%
3 3877
2.1%
4 4002
2.2%
5 4012
2.2%
6 3769
2.1%
7 4044
2.2%
8 4141
2.3%
9 3986
2.2%
ValueCountFrequency (%)
99 8
 
< 0.1%
98 15
 
< 0.1%
97 9
 
< 0.1%
96 11
 
< 0.1%
95 19
 
< 0.1%
94 13
 
< 0.1%
93 20
< 0.1%
92 34
< 0.1%
91 26
< 0.1%
90 49
< 0.1%

PUFC06_MSTAT
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
80708 
2
71967 
18339 
3
 
7207
4
 
2566
Other values (2)
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 80708
44.6%
2 71967
39.8%
18339
 
10.1%
3 7207
 
4.0%
4 2566
 
1.4%
6 51
 
< 0.1%
5 24
 
< 0.1%

Length

2025-03-23T22:46:17.828471image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:17.929250image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 80708
49.7%
2 71967
44.3%
3 7207
 
4.4%
4 2566
 
1.6%
6 51
 
< 0.1%
5 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 80708
44.6%
2 71967
39.8%
18339
 
10.1%
3 7207
 
4.0%
4 2566
 
1.4%
6 51
 
< 0.1%
5 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 80708
44.6%
2 71967
39.8%
18339
 
10.1%
3 7207
 
4.0%
4 2566
 
1.4%
6 51
 
< 0.1%
5 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 80708
44.6%
2 71967
39.8%
18339
 
10.1%
3 7207
 
4.0%
4 2566
 
1.4%
6 51
 
< 0.1%
5 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 80708
44.6%
2 71967
39.8%
18339
 
10.1%
3 7207
 
4.0%
4 2566
 
1.4%
6 51
 
< 0.1%
5 24
 
< 0.1%
Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:18.097106image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters542586
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row350
2nd row350
3rd row350
4th row320
5th row350
ValueCountFrequency (%)
350 33256
20.5%
280 17989
 
11.1%
320 9059
 
5.6%
250 8794
 
5.4%
240 8641
 
5.3%
330 8026
 
4.9%
230 7654
 
4.7%
310 7005
 
4.3%
220 6551
 
4.0%
820 5939
 
3.7%
Other values (57) 49609
30.5%
2025-03-23T22:46:18.361836image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 156071
28.8%
2 85070
15.7%
3 83290
15.4%
55017
 
10.1%
5 48646
 
9.0%
8 39924
 
7.4%
1 28651
 
5.3%
4 22054
 
4.1%
6 20867
 
3.8%
7 2398
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 156071
28.8%
2 85070
15.7%
3 83290
15.4%
55017
 
10.1%
5 48646
 
9.0%
8 39924
 
7.4%
1 28651
 
5.3%
4 22054
 
4.1%
6 20867
 
3.8%
7 2398
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 156071
28.8%
2 85070
15.7%
3 83290
15.4%
55017
 
10.1%
5 48646
 
9.0%
8 39924
 
7.4%
1 28651
 
5.3%
4 22054
 
4.1%
6 20867
 
3.8%
7 2398
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 156071
28.8%
2 85070
15.7%
3 83290
15.4%
55017
 
10.1%
5 48646
 
9.0%
8 39924
 
7.4%
1 28651
 
5.3%
4 22054
 
4.1%
6 20867
 
3.8%
7 2398
 
0.4%

PUFC08_CURSCH
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
107137 
1
51643 
2
22082 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row2
4th row
5th row

Common Values

ValueCountFrequency (%)
107137
59.2%
1 51643
28.6%
2 22082
 
12.2%

Length

2025-03-23T22:46:18.472680image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:18.579274image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 51643
70.0%
2 22082
30.0%

Most occurring characters

ValueCountFrequency (%)
107137
59.2%
1 51643
28.6%
2 22082
 
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
107137
59.2%
1 51643
28.6%
2 22082
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
107137
59.2%
1 51643
28.6%
2 22082
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
107137
59.2%
1 51643
28.6%
2 22082
 
12.2%

PUFC09_GRADTECH
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2
117167 
57782 
1
 
5913

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 117167
64.8%
57782
31.9%
1 5913
 
3.3%

Length

2025-03-23T22:46:18.689847image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:18.780352image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 117167
95.2%
1 5913
 
4.8%

Most occurring characters

ValueCountFrequency (%)
2 117167
64.8%
57782
31.9%
1 5913
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 117167
64.8%
57782
31.9%
1 5913
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 117167
64.8%
57782
31.9%
1 5913
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 117167
64.8%
57782
31.9%
1 5913
 
3.3%

PUFC10_CONWR
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
5
119496 
57782 
1
 
3333
2
 
210
4
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 119496
66.1%
57782
31.9%
1 3333
 
1.8%
2 210
 
0.1%
4 29
 
< 0.1%
3 12
 
< 0.1%

Length

2025-03-23T22:46:18.875729image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:18.975079image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
5 119496
97.1%
1 3333
 
2.7%
2 210
 
0.2%
4 29
 
< 0.1%
3 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
5 119496
66.1%
57782
31.9%
1 3333
 
1.8%
2 210
 
0.1%
4 29
 
< 0.1%
3 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 119496
66.1%
57782
31.9%
1 3333
 
1.8%
2 210
 
0.1%
4 29
 
< 0.1%
3 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 119496
66.1%
57782
31.9%
1 3333
 
1.8%
2 210
 
0.1%
4 29
 
< 0.1%
3 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 119496
66.1%
57782
31.9%
1 3333
 
1.8%
2 210
 
0.1%
4 29
 
< 0.1%
3 12
 
< 0.1%

PUFC11_WORK
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2
87556 
1
71412 
21894 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 87556
48.4%
1 71412
39.5%
21894
 
12.1%

Length

2025-03-23T22:46:19.081615image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:19.172003image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 87556
55.1%
1 71412
44.9%

Most occurring characters

ValueCountFrequency (%)
2 87556
48.4%
1 71412
39.5%
21894
 
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 87556
48.4%
1 71412
39.5%
21894
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 87556
48.4%
1 71412
39.5%
21894
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 87556
48.4%
1 71412
39.5%
21894
 
12.1%

PUFC12_JOB
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
93306 
2
86466 
1
 
1090

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
93306
51.6%
2 86466
47.8%
1 1090
 
0.6%

Length

2025-03-23T22:46:19.271340image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:19.359979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 86466
98.8%
1 1090
 
1.2%

Most occurring characters

ValueCountFrequency (%)
93306
51.6%
2 86466
47.8%
1 1090
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
93306
51.6%
2 86466
47.8%
1 1090
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
93306
51.6%
2 86466
47.8%
1 1090
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
93306
51.6%
2 86466
47.8%
1 1090
 
0.6%

PUFC14_PROCC
Categorical

High correlation 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
108360 
92
 
10226
61
 
8515
14
 
6822
52
 
5744
Other values (39)
41195 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters361724
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row61
2nd row92
3rd row92
4th row61
5th row91

Common Values

ValueCountFrequency (%)
108360
59.9%
92 10226
 
5.7%
61 8515
 
4.7%
14 6822
 
3.8%
52 5744
 
3.2%
93 5390
 
3.0%
13 3968
 
2.2%
91 3048
 
1.7%
83 2823
 
1.6%
51 2793
 
1.5%
Other values (34) 23173
 
12.8%

Length

2025-03-23T22:46:19.470612image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
92 10226
14.1%
61 8515
11.7%
14 6822
 
9.4%
52 5744
 
7.9%
93 5390
 
7.4%
13 3968
 
5.5%
91 3048
 
4.2%
83 2823
 
3.9%
51 2793
 
3.9%
71 2524
 
3.5%
Other values (33) 20649
28.5%

Most occurring characters

ValueCountFrequency (%)
216720
59.9%
1 32167
 
8.9%
2 25939
 
7.2%
9 19916
 
5.5%
3 19326
 
5.3%
4 14081
 
3.9%
5 12435
 
3.4%
6 11681
 
3.2%
7 5454
 
1.5%
8 3832
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
216720
59.9%
1 32167
 
8.9%
2 25939
 
7.2%
9 19916
 
5.5%
3 19326
 
5.3%
4 14081
 
3.9%
5 12435
 
3.4%
6 11681
 
3.2%
7 5454
 
1.5%
8 3832
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
216720
59.9%
1 32167
 
8.9%
2 25939
 
7.2%
9 19916
 
5.5%
3 19326
 
5.3%
4 14081
 
3.9%
5 12435
 
3.4%
6 11681
 
3.2%
7 5454
 
1.5%
8 3832
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
216720
59.9%
1 32167
 
8.9%
2 25939
 
7.2%
9 19916
 
5.5%
3 19326
 
5.3%
4 14081
 
3.9%
5 12435
 
3.4%
6 11681
 
3.2%
7 5454
 
1.5%
8 3832
 
1.1%
Distinct88
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:19.733490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters361724
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row96
ValueCountFrequency (%)
01 17995
24.8%
47 12317
17.0%
41 4778
 
6.6%
49 4463
 
6.2%
84 4301
 
5.9%
96 4258
 
5.9%
03 2859
 
3.9%
56 2440
 
3.4%
85 2239
 
3.1%
10 1390
 
1.9%
Other values (77) 15462
21.3%
2025-03-23T22:46:20.013633image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
216720
59.9%
4 30684
 
8.5%
1 26999
 
7.5%
0 24549
 
6.8%
7 13316
 
3.7%
6 11536
 
3.2%
8 10211
 
2.8%
9 10073
 
2.8%
5 8196
 
2.3%
3 5130
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
216720
59.9%
4 30684
 
8.5%
1 26999
 
7.5%
0 24549
 
6.8%
7 13316
 
3.7%
6 11536
 
3.2%
8 10211
 
2.8%
9 10073
 
2.8%
5 8196
 
2.3%
3 5130
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
216720
59.9%
4 30684
 
8.5%
1 26999
 
7.5%
0 24549
 
6.8%
7 13316
 
3.7%
6 11536
 
3.2%
8 10211
 
2.8%
9 10073
 
2.8%
5 8196
 
2.3%
3 5130
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
216720
59.9%
4 30684
 
8.5%
1 26999
 
7.5%
0 24549
 
6.8%
7 13316
 
3.7%
6 11536
 
3.2%
8 10211
 
2.8%
9 10073
 
2.8%
5 8196
 
2.3%
3 5130
 
1.4%

PUFC17_NATEM
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
109507 
1
53886 
2
14929 
3
 
2540

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
109507
60.5%
1 53886
29.8%
2 14929
 
8.3%
3 2540
 
1.4%

Length

2025-03-23T22:46:20.121477image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:20.216155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 53886
75.5%
2 14929
 
20.9%
3 2540
 
3.6%

Most occurring characters

ValueCountFrequency (%)
109507
60.5%
1 53886
29.8%
2 14929
 
8.3%
3 2540
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
109507
60.5%
1 53886
29.8%
2 14929
 
8.3%
3 2540
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
109507
60.5%
1 53886
29.8%
2 14929
 
8.3%
3 2540
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
109507
60.5%
1 53886
29.8%
2 14929
 
8.3%
3 2540
 
1.4%

PUFC18_PNWHRS
Categorical

High correlation  Imbalance 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
109507 
08
35535 
06
 
5930
10
 
5518
04
 
4684
Other values (12)
19688 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters361724
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08
2nd row04
3rd row08
4th row04
5th row12

Common Values

ValueCountFrequency (%)
109507
60.5%
08 35535
 
19.6%
06 5930
 
3.3%
10 5518
 
3.1%
04 4684
 
2.6%
05 3906
 
2.2%
12 3649
 
2.0%
09 2415
 
1.3%
03 2355
 
1.3%
07 2290
 
1.3%
Other values (7) 5073
 
2.8%

Length

2025-03-23T22:46:20.320019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08 35535
49.8%
06 5930
 
8.3%
10 5518
 
7.7%
04 4684
 
6.6%
05 3906
 
5.5%
12 3649
 
5.1%
09 2415
 
3.4%
03 2355
 
3.3%
07 2290
 
3.2%
02 2173
 
3.0%
Other values (6) 2900
 
4.1%

Most occurring characters

ValueCountFrequency (%)
219014
60.5%
0 65712
 
18.2%
8 35535
 
9.8%
1 12666
 
3.5%
6 6225
 
1.7%
2 5822
 
1.6%
4 5088
 
1.4%
5 4183
 
1.2%
3 2774
 
0.8%
9 2415
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
219014
60.5%
0 65712
 
18.2%
8 35535
 
9.8%
1 12666
 
3.5%
6 6225
 
1.7%
2 5822
 
1.6%
4 5088
 
1.4%
5 4183
 
1.2%
3 2774
 
0.8%
9 2415
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
219014
60.5%
0 65712
 
18.2%
8 35535
 
9.8%
1 12666
 
3.5%
6 6225
 
1.7%
2 5822
 
1.6%
4 5088
 
1.4%
5 4183
 
1.2%
3 2774
 
0.8%
9 2415
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
219014
60.5%
0 65712
 
18.2%
8 35535
 
9.8%
1 12666
 
3.5%
6 6225
 
1.7%
2 5822
 
1.6%
4 5088
 
1.4%
5 4183
 
1.2%
3 2774
 
0.8%
9 2415
 
0.7%
Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:20.499112image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters542586
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row024
2nd row008
3rd row024
4th row020
5th row072
ValueCountFrequency (%)
048 18081
25.3%
040 8888
 
12.5%
056 3191
 
4.5%
024 3049
 
4.3%
060 2646
 
3.7%
042 2279
 
3.2%
070 2150
 
3.0%
036 2135
 
3.0%
030 1966
 
2.8%
016 1649
 
2.3%
Other values (92) 25321
35.5%
2025-03-23T22:46:20.873364image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
328521
60.5%
0 95668
 
17.6%
4 37664
 
6.9%
8 23479
 
4.3%
2 14540
 
2.7%
6 11515
 
2.1%
5 8462
 
1.6%
1 8103
 
1.5%
3 7839
 
1.4%
7 5131
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
328521
60.5%
0 95668
 
17.6%
4 37664
 
6.9%
8 23479
 
4.3%
2 14540
 
2.7%
6 11515
 
2.1%
5 8462
 
1.6%
1 8103
 
1.5%
3 7839
 
1.4%
7 5131
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
328521
60.5%
0 95668
 
17.6%
4 37664
 
6.9%
8 23479
 
4.3%
2 14540
 
2.7%
6 11515
 
2.1%
5 8462
 
1.6%
1 8103
 
1.5%
3 7839
 
1.4%
7 5131
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
328521
60.5%
0 95668
 
17.6%
4 37664
 
6.9%
8 23479
 
4.3%
2 14540
 
2.7%
6 11515
 
2.1%
5 8462
 
1.6%
1 8103
 
1.5%
3 7839
 
1.4%
7 5131
 
0.9%

PUFC20_PWMORE
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
109507 
2
57418 
1
13937 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
109507
60.5%
2 57418
31.7%
1 13937
 
7.7%

Length

2025-03-23T22:46:20.995966image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:21.096495image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 57418
80.5%
1 13937
 
19.5%

Most occurring characters

ValueCountFrequency (%)
109507
60.5%
2 57418
31.7%
1 13937
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 57418
31.7%
1 13937
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 57418
31.7%
1 13937
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 57418
31.7%
1 13937
 
7.7%

PUFC21_PLADDW
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
109507 
2
63881 
1
 
7474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
109507
60.5%
2 63881
35.3%
1 7474
 
4.1%

Length

2025-03-23T22:46:21.192796image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:21.288536image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 63881
89.5%
1 7474
 
10.5%

Most occurring characters

ValueCountFrequency (%)
109507
60.5%
2 63881
35.3%
1 7474
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 63881
35.3%
1 7474
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 63881
35.3%
1 7474
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 63881
35.3%
1 7474
 
4.1%

PUFC22_PFWRK
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
109507 
2
69856 
1
 
1499

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
109507
60.5%
2 69856
38.6%
1 1499
 
0.8%

Length

2025-03-23T22:46:21.394585image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:21.496333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 69856
97.9%
1 1499
 
2.1%

Most occurring characters

ValueCountFrequency (%)
109507
60.5%
2 69856
38.6%
1 1499
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 69856
38.6%
1 1499
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 69856
38.6%
1 1499
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 69856
38.6%
1 1499
 
0.8%

PUFC23_PCLASS
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
109507 
1
32049 
3
20788 
2
 
6327
6
 
6324
Other values (3)
 
5867

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row6
3rd row1
4th row3
5th row0

Common Values

ValueCountFrequency (%)
109507
60.5%
1 32049
 
17.7%
3 20788
 
11.5%
2 6327
 
3.5%
6 6324
 
3.5%
0 3323
 
1.8%
4 2328
 
1.3%
5 216
 
0.1%

Length

2025-03-23T22:46:21.597705image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:21.709452image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 32049
44.9%
3 20788
29.1%
2 6327
 
8.9%
6 6324
 
8.9%
0 3323
 
4.7%
4 2328
 
3.3%
5 216
 
0.3%

Most occurring characters

ValueCountFrequency (%)
109507
60.5%
1 32049
 
17.7%
3 20788
 
11.5%
2 6327
 
3.5%
6 6324
 
3.5%
0 3323
 
1.8%
4 2328
 
1.3%
5 216
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
109507
60.5%
1 32049
 
17.7%
3 20788
 
11.5%
2 6327
 
3.5%
6 6324
 
3.5%
0 3323
 
1.8%
4 2328
 
1.3%
5 216
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
109507
60.5%
1 32049
 
17.7%
3 20788
 
11.5%
2 6327
 
3.5%
6 6324
 
3.5%
0 3323
 
1.8%
4 2328
 
1.3%
5 216
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
109507
60.5%
1 32049
 
17.7%
3 20788
 
11.5%
2 6327
 
3.5%
6 6324
 
3.5%
0 3323
 
1.8%
4 2328
 
1.3%
5 216
 
0.1%

PUFC24_PBASIS
Categorical

Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
138947 
4
17593 
3
16419 
7
 
5327
1
 
837
Other values (4)
 
1739

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row3
4th row
5th row4

Common Values

ValueCountFrequency (%)
138947
76.8%
4 17593
 
9.7%
3 16419
 
9.1%
7 5327
 
2.9%
1 837
 
0.5%
5 722
 
0.4%
6 499
 
0.3%
2 261
 
0.1%
0 257
 
0.1%

Length

2025-03-23T22:46:21.830236image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:21.939611image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 17593
42.0%
3 16419
39.2%
7 5327
 
12.7%
1 837
 
2.0%
5 722
 
1.7%
6 499
 
1.2%
2 261
 
0.6%
0 257
 
0.6%

Most occurring characters

ValueCountFrequency (%)
138947
76.8%
4 17593
 
9.7%
3 16419
 
9.1%
7 5327
 
2.9%
1 837
 
0.5%
5 722
 
0.4%
6 499
 
0.3%
2 261
 
0.1%
0 257
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
138947
76.8%
4 17593
 
9.7%
3 16419
 
9.1%
7 5327
 
2.9%
1 837
 
0.5%
5 722
 
0.4%
6 499
 
0.3%
2 261
 
0.1%
0 257
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
138947
76.8%
4 17593
 
9.7%
3 16419
 
9.1%
7 5327
 
2.9%
1 837
 
0.5%
5 722
 
0.4%
6 499
 
0.3%
2 261
 
0.1%
0 257
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
138947
76.8%
4 17593
 
9.7%
3 16419
 
9.1%
7 5327
 
2.9%
1 837
 
0.5%
5 722
 
0.4%
6 499
 
0.3%
2 261
 
0.1%
0 257
 
0.1%
Distinct1152
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:22.381810image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters904310
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique337 ?
Unique (%)0.2%

Sample

1st row
2nd row
3rd row00250
4th row
5th row00115
ValueCountFrequency (%)
00200 3216
 
8.8%
00300 2834
 
7.7%
00250 2622
 
7.2%
00481 1756
 
4.8%
00350 1666
 
4.6%
00150 1618
 
4.4%
00400 1210
 
3.3%
00500 1178
 
3.2%
00100 1100
 
3.0%
00353 465
 
1.3%
Other values (1141) 18923
51.7%
2025-03-23T22:46:22.804085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
721370
79.8%
0 106007
 
11.7%
5 13639
 
1.5%
1 12403
 
1.4%
2 11995
 
1.3%
3 11938
 
1.3%
4 7358
 
0.8%
8 7016
 
0.8%
6 5043
 
0.6%
7 4112
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 904310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
721370
79.8%
0 106007
 
11.7%
5 13639
 
1.5%
1 12403
 
1.4%
2 11995
 
1.3%
3 11938
 
1.3%
4 7358
 
0.8%
8 7016
 
0.8%
6 5043
 
0.6%
7 4112
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 904310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
721370
79.8%
0 106007
 
11.7%
5 13639
 
1.5%
1 12403
 
1.4%
2 11995
 
1.3%
3 11938
 
1.3%
4 7358
 
0.8%
8 7016
 
0.8%
6 5043
 
0.6%
7 4112
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 904310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
721370
79.8%
0 106007
 
11.7%
5 13639
 
1.5%
1 12403
 
1.4%
2 11995
 
1.3%
3 11938
 
1.3%
4 7358
 
0.8%
8 7016
 
0.8%
6 5043
 
0.6%
7 4112
 
0.5%

PUFC26_OJOB
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
109507 
2
65417 
1
 
5938

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
109507
60.5%
2 65417
36.2%
1 5938
 
3.3%

Length

2025-03-23T22:46:22.923036image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:23.013072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 65417
91.7%
1 5938
 
8.3%

Most occurring characters

ValueCountFrequency (%)
109507
60.5%
2 65417
36.2%
1 5938
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 65417
36.2%
1 5938
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 65417
36.2%
1 5938
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
109507
60.5%
2 65417
36.2%
1 5938
 
3.3%

PUFC27_NJOBS
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
174924 
1
 
5402
2
 
507
3
 
23
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row1
4th row2
5th row

Common Values

ValueCountFrequency (%)
174924
96.7%
1 5402
 
3.0%
2 507
 
0.3%
3 23
 
< 0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%

Length

2025-03-23T22:46:23.108256image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:23.202541image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5402
91.0%
2 507
 
8.5%
3 23
 
0.4%
4 4
 
0.1%
5 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
174924
96.7%
1 5402
 
3.0%
2 507
 
0.3%
3 23
 
< 0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
174924
96.7%
1 5402
 
3.0%
2 507
 
0.3%
3 23
 
< 0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
174924
96.7%
1 5402
 
3.0%
2 507
 
0.3%
3 23
 
< 0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
174924
96.7%
1 5402
 
3.0%
2 507
 
0.3%
3 23
 
< 0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%
Distinct111
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:23.392407image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters542586
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row032
2nd row008
3rd row034
4th row033
5th row072
ValueCountFrequency (%)
048 18094
25.4%
040 8624
 
12.1%
056 3412
 
4.8%
024 2717
 
3.8%
060 2710
 
3.8%
042 2240
 
3.1%
070 2161
 
3.0%
036 2037
 
2.9%
030 1875
 
2.6%
072 1582
 
2.2%
Other values (100) 25903
36.3%
2025-03-23T22:46:23.681734image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
328521
60.5%
0 94576
 
17.4%
4 37624
 
6.9%
8 23434
 
4.3%
2 14176
 
2.6%
6 11850
 
2.2%
5 9392
 
1.7%
3 8060
 
1.5%
1 7717
 
1.4%
7 5359
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
328521
60.5%
0 94576
 
17.4%
4 37624
 
6.9%
8 23434
 
4.3%
2 14176
 
2.6%
6 11850
 
2.2%
5 9392
 
1.7%
3 8060
 
1.5%
1 7717
 
1.4%
7 5359
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
328521
60.5%
0 94576
 
17.4%
4 37624
 
6.9%
8 23434
 
4.3%
2 14176
 
2.6%
6 11850
 
2.2%
5 9392
 
1.7%
3 8060
 
1.5%
1 7717
 
1.4%
7 5359
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
328521
60.5%
0 94576
 
17.4%
4 37624
 
6.9%
8 23434
 
4.3%
2 14176
 
2.6%
6 11850
 
2.2%
5 9392
 
1.7%
3 8060
 
1.5%
1 7717
 
1.4%
7 5359
 
1.0%

PUFC29_WWM48H
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
163629 
1
 
10587
2
 
6352
4
 
164
3
 
103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row2

Common Values

ValueCountFrequency (%)
163629
90.5%
1 10587
 
5.9%
2 6352
 
3.5%
4 164
 
0.1%
3 103
 
0.1%
5 27
 
< 0.1%

Length

2025-03-23T22:46:23.794645image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:23.889908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10587
61.4%
2 6352
36.9%
4 164
 
1.0%
3 103
 
0.6%
5 27
 
0.2%

Most occurring characters

ValueCountFrequency (%)
163629
90.5%
1 10587
 
5.9%
2 6352
 
3.5%
4 164
 
0.1%
3 103
 
0.1%
5 27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
163629
90.5%
1 10587
 
5.9%
2 6352
 
3.5%
4 164
 
0.1%
3 103
 
0.1%
5 27
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
163629
90.5%
1 10587
 
5.9%
2 6352
 
3.5%
4 164
 
0.1%
3 103
 
0.1%
5 27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
163629
90.5%
1 10587
 
5.9%
2 6352
 
3.5%
4 164
 
0.1%
3 103
 
0.1%
5 27
 
< 0.1%

PUFC30_LOOKW
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
132692 
2
45877 
1
 
2293

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
132692
73.4%
2 45877
 
25.4%
1 2293
 
1.3%

Length

2025-03-23T22:46:23.993986image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:24.083994image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 45877
95.2%
1 2293
 
4.8%

Most occurring characters

ValueCountFrequency (%)
132692
73.4%
2 45877
 
25.4%
1 2293
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
132692
73.4%
2 45877
 
25.4%
1 2293
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
132692
73.4%
2 45877
 
25.4%
1 2293
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
132692
73.4%
2 45877
 
25.4%
1 2293
 
1.3%

PUFC31_FLWRK
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
178569 
2
 
1838
1
 
455

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
178569
98.7%
2 1838
 
1.0%
1 455
 
0.3%

Length

2025-03-23T22:46:24.180835image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:24.268535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1838
80.2%
1 455
 
19.8%

Most occurring characters

ValueCountFrequency (%)
178569
98.7%
2 1838
 
1.0%
1 455
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
178569
98.7%
2 1838
 
1.0%
1 455
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
178569
98.7%
2 1838
 
1.0%
1 455
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
178569
98.7%
2 1838
 
1.0%
1 455
 
0.3%

PUFC32_JOBSM
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
178569 
4
 
761
3
 
724
2
 
448
1
 
189
Other values (2)
 
171

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
178569
98.7%
4 761
 
0.4%
3 724
 
0.4%
2 448
 
0.2%
1 189
 
0.1%
5 114
 
0.1%
6 57
 
< 0.1%

Length

2025-03-23T22:46:24.362328image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:24.461725image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
4 761
33.2%
3 724
31.6%
2 448
19.5%
1 189
 
8.2%
5 114
 
5.0%
6 57
 
2.5%

Most occurring characters

ValueCountFrequency (%)
178569
98.7%
4 761
 
0.4%
3 724
 
0.4%
2 448
 
0.2%
1 189
 
0.1%
5 114
 
0.1%
6 57
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
178569
98.7%
4 761
 
0.4%
3 724
 
0.4%
2 448
 
0.2%
1 189
 
0.1%
5 114
 
0.1%
6 57
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
178569
98.7%
4 761
 
0.4%
3 724
 
0.4%
2 448
 
0.2%
1 189
 
0.1%
5 114
 
0.1%
6 57
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
178569
98.7%
4 761
 
0.4%
3 724
 
0.4%
2 448
 
0.2%
1 189
 
0.1%
5 114
 
0.1%
6 57
 
< 0.1%

PUFC33_WEEKS
Categorical

High correlation  Imbalance 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
178569 
002
 
581
001
 
578
004
 
328
003
 
314
Other values (31)
 
492

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters542586
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
178569
98.7%
002 581
 
0.3%
001 578
 
0.3%
004 328
 
0.2%
003 314
 
0.2%
008 113
 
0.1%
005 72
 
< 0.1%
012 69
 
< 0.1%
006 68
 
< 0.1%
010 31
 
< 0.1%
Other values (26) 139
 
0.1%

Length

2025-03-23T22:46:24.567693image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
002 581
25.3%
001 578
25.2%
004 328
14.3%
003 314
13.7%
008 113
 
4.9%
005 72
 
3.1%
012 69
 
3.0%
006 68
 
3.0%
010 31
 
1.4%
016 24
 
1.0%
Other values (25) 115
 
5.0%

Most occurring characters

ValueCountFrequency (%)
535707
98.7%
0 4428
 
0.8%
1 730
 
0.1%
2 695
 
0.1%
4 353
 
0.1%
3 323
 
0.1%
8 125
 
< 0.1%
6 103
 
< 0.1%
5 84
 
< 0.1%
7 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
535707
98.7%
0 4428
 
0.8%
1 730
 
0.1%
2 695
 
0.1%
4 353
 
0.1%
3 323
 
0.1%
8 125
 
< 0.1%
6 103
 
< 0.1%
5 84
 
< 0.1%
7 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
535707
98.7%
0 4428
 
0.8%
1 730
 
0.1%
2 695
 
0.1%
4 353
 
0.1%
3 323
 
0.1%
8 125
 
< 0.1%
6 103
 
< 0.1%
5 84
 
< 0.1%
7 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
535707
98.7%
0 4428
 
0.8%
1 730
 
0.1%
2 695
 
0.1%
4 353
 
0.1%
3 323
 
0.1%
8 125
 
< 0.1%
6 103
 
< 0.1%
5 84
 
< 0.1%
7 22
 
< 0.1%

PUFC34_WYNOT
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
134985 
7
20584 
8
 
12852
6
 
7583
9
 
1182
Other values (5)
 
3676

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
134985
74.6%
7 20584
 
11.4%
8 12852
 
7.1%
6 7583
 
4.2%
9 1182
 
0.7%
3 1177
 
0.7%
1 849
 
0.5%
5 792
 
0.4%
2 744
 
0.4%
4 114
 
0.1%

Length

2025-03-23T22:46:24.671655image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:24.788922image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
7 20584
44.9%
8 12852
28.0%
6 7583
 
16.5%
9 1182
 
2.6%
3 1177
 
2.6%
1 849
 
1.9%
5 792
 
1.7%
2 744
 
1.6%
4 114
 
0.2%

Most occurring characters

ValueCountFrequency (%)
134985
74.6%
7 20584
 
11.4%
8 12852
 
7.1%
6 7583
 
4.2%
9 1182
 
0.7%
3 1177
 
0.7%
1 849
 
0.5%
5 792
 
0.4%
2 744
 
0.4%
4 114
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
134985
74.6%
7 20584
 
11.4%
8 12852
 
7.1%
6 7583
 
4.2%
9 1182
 
0.7%
3 1177
 
0.7%
1 849
 
0.5%
5 792
 
0.4%
2 744
 
0.4%
4 114
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
134985
74.6%
7 20584
 
11.4%
8 12852
 
7.1%
6 7583
 
4.2%
9 1182
 
0.7%
3 1177
 
0.7%
1 849
 
0.5%
5 792
 
0.4%
2 744
 
0.4%
4 114
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
134985
74.6%
7 20584
 
11.4%
8 12852
 
7.1%
6 7583
 
4.2%
9 1182
 
0.7%
3 1177
 
0.7%
1 849
 
0.5%
5 792
 
0.4%
2 744
 
0.4%
4 114
 
0.1%

PUFC35_LTLOOKW
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
179269 
1
 
662
3
 
485
2
 
446

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
179269
99.1%
1 662
 
0.4%
3 485
 
0.3%
2 446
 
0.2%

Length

2025-03-23T22:46:24.915647image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:25.007788image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 662
41.6%
3 485
30.4%
2 446
28.0%

Most occurring characters

ValueCountFrequency (%)
179269
99.1%
1 662
 
0.4%
3 485
 
0.3%
2 446
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
179269
99.1%
1 662
 
0.4%
3 485
 
0.3%
2 446
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
179269
99.1%
1 662
 
0.4%
3 485
 
0.3%
2 446
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
179269
99.1%
1 662
 
0.4%
3 485
 
0.3%
2 446
 
0.2%

PUFC36_AVAIL
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
174893 
1
 
4293
2
 
1676

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
174893
96.7%
1 4293
 
2.4%
2 1676
 
0.9%

Length

2025-03-23T22:46:25.105059image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:25.192036image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 4293
71.9%
2 1676
 
28.1%

Most occurring characters

ValueCountFrequency (%)
174893
96.7%
1 4293
 
2.4%
2 1676
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
174893
96.7%
1 4293
 
2.4%
2 1676
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
174893
96.7%
1 4293
 
2.4%
2 1676
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
174893
96.7%
1 4293
 
2.4%
2 1676
 
0.9%

PUFC37_WILLING
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
174893 
1
 
4586
2
 
1383

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
174893
96.7%
1 4586
 
2.5%
2 1383
 
0.8%

Length

2025-03-23T22:46:25.285259image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:25.379641image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 4586
76.8%
2 1383
 
23.2%

Most occurring characters

ValueCountFrequency (%)
174893
96.7%
1 4586
 
2.5%
2 1383
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
174893
96.7%
1 4586
 
2.5%
2 1383
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
174893
96.7%
1 4586
 
2.5%
2 1383
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
174893
96.7%
1 4586
 
2.5%
2 1383
 
0.8%

PUFC38_PREVJOB
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
132692 
1
27880 
2
20290 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
132692
73.4%
1 27880
 
15.4%
2 20290
 
11.2%

Length

2025-03-23T22:46:25.474488image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:25.564831image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 27880
57.9%
2 20290
42.1%

Most occurring characters

ValueCountFrequency (%)
132692
73.4%
1 27880
 
15.4%
2 20290
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
132692
73.4%
1 27880
 
15.4%
2 20290
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
132692
73.4%
1 27880
 
15.4%
2 20290
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
132692
73.4%
1 27880
 
15.4%
2 20290
 
11.2%

PUFC40_POCC
Categorical

High correlation  Imbalance 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
152982 
52
 
4963
91
 
4275
92
 
3878
61
 
1830
Other values (39)
 
12934

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters361724
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
152982
84.6%
52 4963
 
2.7%
91 4275
 
2.4%
92 3878
 
2.1%
61 1830
 
1.0%
93 1696
 
0.9%
51 1460
 
0.8%
14 1039
 
0.6%
75 883
 
0.5%
41 741
 
0.4%
Other values (34) 7115
 
3.9%

Length

2025-03-23T22:46:25.666618image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
52 4963
17.8%
91 4275
15.3%
92 3878
13.9%
61 1830
 
6.6%
93 1696
 
6.1%
51 1460
 
5.2%
14 1039
 
3.7%
75 883
 
3.2%
41 741
 
2.7%
83 616
 
2.2%
Other values (33) 6499
23.3%

Most occurring characters

ValueCountFrequency (%)
305964
84.6%
2 11664
 
3.2%
1 11165
 
3.1%
9 10291
 
2.8%
5 8217
 
2.3%
3 5405
 
1.5%
4 3436
 
0.9%
6 2419
 
0.7%
7 1900
 
0.5%
8 1116
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
305964
84.6%
2 11664
 
3.2%
1 11165
 
3.1%
9 10291
 
2.8%
5 8217
 
2.3%
3 5405
 
1.5%
4 3436
 
0.9%
6 2419
 
0.7%
7 1900
 
0.5%
8 1116
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
305964
84.6%
2 11664
 
3.2%
1 11165
 
3.1%
9 10291
 
2.8%
5 8217
 
2.3%
3 5405
 
1.5%
4 3436
 
0.9%
6 2419
 
0.7%
7 1900
 
0.5%
8 1116
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
305964
84.6%
2 11664
 
3.2%
1 11165
 
3.1%
9 10291
 
2.8%
5 8217
 
2.3%
3 5405
 
1.5%
4 3436
 
0.9%
6 2419
 
0.7%
7 1900
 
0.5%
8 1116
 
0.3%

PUFC41_WQTR
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
81627 
1
73037 
2
26198 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
81627
45.1%
1 73037
40.4%
2 26198
 
14.5%

Length

2025-03-23T22:46:25.767967image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:25.864311image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 73037
73.6%
2 26198
 
26.4%

Most occurring characters

ValueCountFrequency (%)
81627
45.1%
1 73037
40.4%
2 26198
 
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81627
45.1%
1 73037
40.4%
2 26198
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81627
45.1%
1 73037
40.4%
2 26198
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81627
45.1%
1 73037
40.4%
2 26198
 
14.5%
Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-03-23T22:46:26.094171image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters361724
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row96
ValueCountFrequency (%)
01 18499
25.3%
47 12105
16.6%
41 4813
 
6.6%
49 4471
 
6.1%
84 4216
 
5.8%
96 4199
 
5.7%
03 2796
 
3.8%
56 2523
 
3.5%
85 2304
 
3.2%
10 1444
 
2.0%
Other values (78) 15667
21.5%
2025-03-23T22:46:26.361925image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
215650
59.6%
4 30343
 
8.4%
1 27639
 
7.6%
0 24979
 
6.9%
7 13351
 
3.7%
6 11607
 
3.2%
9 10253
 
2.8%
8 10249
 
2.8%
5 8345
 
2.3%
3 5039
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
215650
59.6%
4 30343
 
8.4%
1 27639
 
7.6%
0 24979
 
6.9%
7 13351
 
3.7%
6 11607
 
3.2%
9 10253
 
2.8%
8 10249
 
2.8%
5 8345
 
2.3%
3 5039
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
215650
59.6%
4 30343
 
8.4%
1 27639
 
7.6%
0 24979
 
6.9%
7 13351
 
3.7%
6 11607
 
3.2%
9 10253
 
2.8%
8 10249
 
2.8%
5 8345
 
2.3%
3 5039
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
215650
59.6%
4 30343
 
8.4%
1 27639
 
7.6%
0 24979
 
6.9%
7 13351
 
3.7%
6 11607
 
3.2%
9 10253
 
2.8%
8 10249
 
2.8%
5 8345
 
2.3%
3 5039
 
1.4%

PUFNEWEMPSTAT
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
71355 
61337 
3
43877 
2
 
4293

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180862
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 71355
39.5%
61337
33.9%
3 43877
24.3%
2 4293
 
2.4%

Length

2025-03-23T22:46:26.472022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T22:46:26.567852image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 71355
59.7%
3 43877
36.7%
2 4293
 
3.6%

Most occurring characters

ValueCountFrequency (%)
1 71355
39.5%
61337
33.9%
3 43877
24.3%
2 4293
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 71355
39.5%
61337
33.9%
3 43877
24.3%
2 4293
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 71355
39.5%
61337
33.9%
3 43877
24.3%
2 4293
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 71355
39.5%
61337
33.9%
3 43877
24.3%
2 4293
 
2.4%

Interactions

2025-03-23T22:46:11.021835image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:02.402970image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.395505image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.338734image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:05.280548image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.287518image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.236404image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.219052image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.146270image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.083190image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.142758image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:02.508598image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.495405image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2025-03-23T22:46:07.345552image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.317632image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.247232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.182559image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.234880image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:02.609106image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.587097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.533507image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:05.489286image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.479817image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.446681image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.407866image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.337915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.276199image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.330660image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:02.705247image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.679960image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.625949image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:05.589588image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.574674image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.543998image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.501284image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.432007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.370573image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.439248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:02.813531image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.783496image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.729780image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:05.694920image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.677692image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.649905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.602181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.533634image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.473997image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.535655image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:02.913897image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.873584image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.818710image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:05.791466image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.765835image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.744125image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.691249image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.624488image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2025-03-23T22:46:05.893607image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.862482image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.843690image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.786736image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.722996image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.659635image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.725614image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.114276image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.063774image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2025-03-23T22:46:05.991715image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.954082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.935171image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.875169image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.810321image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.748932image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.819431image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.206479image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.155200image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:05.098501image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.089796image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.047135image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.029549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.963522image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.902570image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.843162image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:11.911066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:03.296952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:04.247858image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:05.189174image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:06.188179image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:07.140825image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:08.122082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.053255image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:09.991488image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-23T22:46:10.930696image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-03-23T22:46:27.201233image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
PUFC01_LNOPUFC03_RELPUFC04_SEXPUFC05_AGEPUFC06_MSTATPUFC08_CURSCHPUFC09_GRADTECHPUFC10_CONWRPUFC11_WORKPUFC12_JOBPUFC14_PROCCPUFC17_NATEMPUFC18_PNWHRSPUFC20_PWMOREPUFC21_PLADDWPUFC22_PFWRKPUFC23_PCLASSPUFC24_PBASISPUFC26_OJOBPUFC27_NJOBSPUFC29_WWM48HPUFC30_LOOKWPUFC31_FLWRKPUFC32_JOBSMPUFC33_WEEKSPUFC34_WYNOTPUFC35_LTLOOKWPUFC36_AVAILPUFC37_WILLINGPUFC38_PREVJOBPUFC40_POCCPUFC41_WQTRPUFHHNUMPUFHHSIZEPUFNEWEMPSTATPUFPRRCDPUFPRVPUFPSUPUFPWGTFINPUFREGPUFRPLPUFURB2K10
PUFC01_LNO1.0000.8420.009-0.6940.2220.2760.3440.2180.2870.1420.1270.2180.1250.2640.2640.2660.1460.0770.2650.0540.0710.0710.0170.0040.0000.0690.0060.0210.0210.1280.0550.3440.0110.4990.283-0.012-0.012-0.0070.0070.0110.0060.022
PUFC03_REL0.8421.0000.084-0.6210.3870.4490.4470.2840.3570.2120.2330.2910.1740.3470.3460.3530.2660.1190.3490.0760.1550.1340.0440.0240.0210.1520.0280.0540.0550.2320.1080.480-0.0090.3210.373-0.006-0.006-0.0100.013-0.0080.0080.083
PUFC04_SEX0.0090.0841.0000.0460.1240.0190.0410.0160.1900.1810.3690.1820.1970.1830.1830.1770.2560.1880.1820.0830.0450.2200.0180.0170.0150.3050.0260.0430.0430.2210.2740.2230.0070.0040.2480.0130.0120.0000.0200.0120.0020.010
PUFC05_AGE-0.694-0.6210.0461.0000.4460.6460.6180.3930.5510.3270.2250.3680.2140.4380.4380.4490.2560.1620.4400.0780.1240.2850.0990.0560.0440.2720.0530.1230.1260.3790.1670.600-0.032-0.2130.5210.0080.008-0.009-0.027-0.0320.0020.063
PUFC06_MSTAT0.2220.3870.1240.4461.0000.5680.4940.3130.7020.3640.2130.2870.2010.3430.3430.3520.2120.1190.3450.0700.1000.1750.0490.0260.0250.2250.0320.0600.0600.2760.1490.4940.0240.0930.4010.0160.0160.0080.0120.0270.0070.036
PUFC08_CURSCH0.2760.4490.0190.6460.5681.0000.3740.3750.4340.4280.3420.3420.3290.3270.3270.3430.3490.2340.3290.0890.1440.1010.1000.0920.0920.3370.0740.1240.1240.2340.1760.4380.0400.1410.3980.0330.0340.0090.0200.0440.0040.029
PUFC09_GRADTECH0.3440.4470.0410.6180.4940.3741.0000.7080.4180.1830.3890.3920.3920.3920.3920.3920.3930.2680.3920.0900.1580.2950.0600.0590.0590.2870.0460.0920.0920.2950.2140.5350.0460.1260.6780.0360.0360.0060.0190.0540.0040.059
PUFC10_CONWR0.2180.2840.0160.3930.3130.3750.7081.0000.5140.1980.2500.3340.2580.4090.4090.4090.2590.1760.4090.0590.1040.3050.0570.0360.0350.1870.0390.0930.0930.3050.1370.5590.0370.0820.5770.0230.0230.0070.0150.0420.0030.052
PUFC11_WORK0.2870.3570.1900.5510.7020.4340.4180.5141.0000.7070.6980.6890.6890.6890.6890.6890.6890.4680.6890.1610.2840.4400.0830.0830.0820.4250.0690.1350.1350.4400.3110.6790.0290.0970.7580.0300.0300.0080.0090.0280.0060.006
PUFC12_JOB0.1420.2120.1810.3270.3640.4280.1830.1980.7071.0000.5700.5470.5480.5470.5470.5470.5510.3730.5470.1260.2220.4450.0840.0840.0830.4310.0700.1360.1360.4450.3150.5150.0290.0670.5840.0320.0320.0080.0190.0330.0050.009
PUFC14_PROCC0.1270.2330.3690.2250.2130.3420.3890.2500.6980.5701.0000.6150.2950.7070.7060.7030.6680.4000.7140.1370.2870.3480.0640.0350.0030.1580.0420.1060.1060.3480.0510.6590.0840.0500.5700.0660.0660.0280.0370.0960.0100.272
PUFC17_NATEM0.2180.2910.1820.3680.2870.3420.3920.3340.6890.5470.6151.0000.5820.7110.7120.7110.6000.4420.7080.1340.2410.3440.0650.0520.0510.2720.0440.1050.1050.3440.1980.6690.0420.0840.5770.0400.0400.0140.0150.0470.0050.061
PUFC18_PNWHRS0.1250.1740.1970.2140.2010.3290.3920.2580.6890.5480.2950.5821.0000.7140.7120.7070.4370.2860.7140.1200.3500.3440.0640.0360.0180.1570.0430.1050.1050.3440.0850.6680.0540.0470.5770.0440.0440.0160.0210.0610.0090.154
PUFC20_PWMORE0.2640.3470.1830.4380.3430.3270.3920.4090.6890.5470.7070.7110.7141.0000.8090.7070.7090.4920.7300.2390.2910.3440.0650.0640.0630.3330.0540.1050.1050.3440.2430.6680.0550.0980.7070.0580.0580.0180.0170.0730.0080.078
PUFC21_PLADDW0.2640.3460.1830.4380.3430.3270.3920.4090.6890.5470.7060.7120.7120.8091.0000.7070.7080.4890.7410.2710.2880.3440.0650.0640.0630.3330.0540.1050.1050.3440.2430.6680.0580.0980.7070.0600.0610.0230.0220.0700.0090.079
PUFC22_PFWRK0.2660.3530.1770.4490.3520.3430.3920.4090.6890.5470.7030.7110.7070.7070.7071.0000.7100.4830.7080.1630.2860.3440.0650.0640.0630.3330.0540.1050.1050.3440.2430.6680.0440.0990.7070.0360.0360.0150.0140.0430.0020.022
PUFC23_PCLASS0.1460.2660.2560.2560.2120.3490.3930.2590.6890.5510.6680.6000.4370.7090.7080.7101.0000.4000.7130.1170.2510.3440.0640.0370.0320.1780.0430.1050.1050.3440.1290.6680.0640.0580.5770.0450.0460.0200.0250.0720.0050.158
PUFC24_PBASIS0.0770.1190.1880.1620.1190.2340.2680.1760.4680.3730.4000.4420.2860.4920.4890.4830.4001.0000.4880.0520.1550.2340.0440.0250.0170.1130.0290.0710.0710.2340.0810.4540.0630.0300.3930.0530.0530.0150.0190.0700.0100.150
PUFC26_OJOB0.2650.3490.1820.4400.3450.3290.3920.4090.6890.5470.7140.7080.7140.7300.7410.7080.7130.4881.0000.7070.3000.3440.0650.0640.0630.3330.0540.1050.1050.3440.2430.6680.0630.0980.7070.0500.0490.0140.0250.0690.0030.095
PUFC27_NJOBS0.0540.0760.0830.0780.0700.0890.0900.0590.1610.1260.1370.1340.1200.2390.2710.1630.1170.0520.7071.0000.0850.0780.0140.0070.0000.0480.0090.0230.0230.0780.0320.1560.0350.0180.1320.0280.0270.0070.0150.0400.0000.091
PUFC29_WWM48H0.0710.1550.0450.1240.1000.1440.1580.1040.2840.2220.2870.2410.3500.2910.2880.2860.2510.1550.3000.0851.0000.1380.0250.0150.0090.0840.0170.0420.0420.1380.0600.2720.0280.0270.2320.0220.0230.0090.0090.0320.0040.060
PUFC30_LOOKW0.0710.1340.2200.2850.1750.1010.2950.3050.4400.4450.3480.3440.3440.3440.3440.3440.3440.2340.3440.0780.1381.0000.7070.7070.7070.7070.1140.4850.4940.7090.5110.4680.0320.0290.8340.0310.0310.0110.0180.0400.0060.047
PUFC31_FLWRK0.0170.0440.0180.0990.0490.1000.0600.0570.0830.0840.0640.0650.0640.0650.0650.0650.0640.0440.0650.0140.0250.7071.0000.7140.7120.0460.0060.4640.4730.1790.1820.1020.0230.0080.4540.0210.0210.0050.0120.0280.0030.035
PUFC32_JOBSM0.0040.0240.0170.0560.0260.0920.0590.0360.0830.0840.0350.0520.0360.0640.0640.0640.0370.0250.0640.0070.0150.7070.7141.0000.4230.0260.0020.4650.4740.1410.1020.0770.0180.0020.3710.0160.0160.0070.0100.0210.0060.041
PUFC33_WEEKS0.0000.0210.0150.0440.0250.0920.0590.0350.0820.0830.0030.0510.0180.0630.0630.0630.0320.0170.0630.0000.0090.7070.7120.4231.0000.0170.0000.4660.4760.1400.0530.0790.0140.0000.3710.0120.0120.0000.0000.0170.0070.036
PUFC34_WYNOT0.0690.1520.3050.2720.2250.3370.2870.1870.4250.4310.1580.2720.1570.3330.3330.3330.1780.1130.3330.0480.0840.7070.0460.0260.0171.0000.6230.6450.6660.8040.3040.5510.0230.0460.6700.0210.0210.0100.0130.0270.0060.045
PUFC35_LTLOOKW0.0060.0280.0260.0530.0320.0740.0460.0390.0690.0700.0420.0440.0430.0540.0540.0540.0430.0290.0540.0090.0170.1140.0060.0020.0000.6231.0000.3710.3720.1140.1030.0710.0120.0100.2670.0160.0160.0000.0070.0120.0050.001
PUFC36_AVAIL0.0210.0540.0430.1230.0600.1240.0920.0930.1350.1360.1060.1050.1050.1050.1050.1050.1050.0710.1050.0230.0420.4850.4640.4650.4660.6450.3711.0000.8990.2370.2670.1410.0220.0130.7170.0180.0180.0080.0140.0250.0060.030
PUFC37_WILLING0.0210.0550.0430.1260.0600.1240.0920.0930.1350.1360.1060.1050.1050.1050.1050.1050.1050.0710.1050.0230.0420.4940.4730.4740.4760.6660.3720.8991.0000.2370.2680.1410.0240.0130.6750.0180.0180.0070.0130.0260.0050.028
PUFC38_PREVJOB0.1280.2320.2210.3790.2760.2340.2950.3050.4400.4450.3480.3440.3440.3440.3440.3440.3440.2340.3440.0780.1380.7090.1790.1410.1400.8040.1140.2370.2371.0000.7070.6820.0640.0760.7120.0450.0460.0270.0290.0670.0050.038
PUFC40_POCC0.0550.1080.2740.1670.1490.1760.2140.1370.3110.3150.0510.1980.0850.2430.2430.2430.1290.0810.2430.0320.0600.5110.1820.1020.0530.3040.1030.2670.2680.7071.0000.6350.0540.0360.4270.0370.0370.0210.0290.0640.0080.150
PUFC41_WQTR0.3440.4800.2230.6000.4940.4380.5350.5590.6790.5150.6590.6690.6680.6680.6680.6680.6680.4540.6680.1560.2720.4680.1020.0770.0790.5510.0710.1410.1410.6820.6351.0000.0570.1460.7990.0380.0390.0170.0230.0590.0030.040
PUFHHNUM0.011-0.0090.007-0.0320.0240.0400.0460.0370.0290.0290.0840.0420.0540.0550.0580.0440.0640.0630.0630.0350.0280.0320.0230.0180.0140.0230.0120.0220.0240.0640.0540.0571.0000.0240.0350.2180.218-0.275-0.3500.9970.0150.520
PUFHHSIZE0.4990.3210.004-0.2130.0930.1410.1260.0820.0970.0670.0500.0840.0470.0980.0980.0990.0580.0300.0980.0180.0270.0290.0080.0020.0000.0460.0100.0130.0130.0760.0360.1460.0241.0000.106-0.023-0.022-0.015-0.0140.0240.0270.048
PUFNEWEMPSTAT0.2830.3730.2480.5210.4010.3980.6780.5770.7580.5840.5700.5770.5770.7070.7070.7070.5770.3930.7070.1320.2320.8340.4540.3710.3710.6700.2670.7170.6750.7120.4270.7990.0350.1061.0000.0320.0320.0100.0190.0410.0060.053
PUFPRRCD-0.012-0.0060.0130.0080.0160.0330.0360.0230.0300.0320.0660.0400.0440.0580.0600.0360.0450.0530.0500.0280.0220.0310.0210.0160.0120.0210.0160.0180.0180.0450.0370.0380.218-0.0230.0321.0001.000-0.115-0.1330.1810.0140.367
PUFPRV-0.012-0.0060.0120.0080.0160.0340.0360.0230.0300.0320.0660.0400.0440.0580.0610.0360.0460.0530.0490.0270.0230.0310.0210.0160.0120.0210.0160.0180.0180.0460.0370.0390.218-0.0220.0321.0001.000-0.113-0.1300.1810.0140.367
PUFPSU-0.007-0.0100.000-0.0090.0080.0090.0060.0070.0080.0080.0280.0140.0160.0180.0230.0150.0200.0150.0140.0070.0090.0110.0050.0070.0000.0100.0000.0080.0070.0270.0210.017-0.275-0.0150.010-0.115-0.1131.0000.630-0.2650.0370.098
PUFPWGTFIN0.0070.0130.020-0.0270.0120.0200.0190.0150.0090.0190.0370.0150.0210.0170.0220.0140.0250.0190.0250.0150.0090.0180.0120.0100.0000.0130.0070.0140.0130.0290.0290.023-0.350-0.0140.019-0.133-0.1300.6301.000-0.3360.0580.104
PUFREG0.011-0.0080.012-0.0320.0270.0440.0540.0420.0280.0330.0960.0470.0610.0730.0700.0430.0720.0700.0690.0400.0320.0400.0280.0210.0170.0270.0120.0250.0260.0670.0640.0590.9970.0240.0410.1810.181-0.265-0.3361.0000.0130.576
PUFRPL0.0060.0080.0020.0020.0070.0040.0040.0030.0060.0050.0100.0050.0090.0080.0090.0020.0050.0100.0030.0000.0040.0060.0030.0060.0070.0060.0050.0060.0050.0050.0080.0030.0150.0270.0060.0140.0140.0370.0580.0131.0000.014
PUFURB2K100.0220.0830.0100.0630.0360.0290.0590.0520.0060.0090.2720.0610.1540.0780.0790.0220.1580.1500.0950.0910.0600.0470.0350.0410.0360.0450.0010.0300.0280.0380.1500.0400.5200.0480.0530.3670.3670.0980.1040.5760.0141.000

Missing values

2025-03-23T22:46:12.234187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-23T22:46:13.171348image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PUFREGPUFPRVPUFPRRCDPUFHHNUMPUFURB2K10PUFPWGTFINPUFSVYMOPUFSVYYRPUFPSUPUFRPLPUFHHSIZEPUFC01_LNOPUFC03_RELPUFC04_SEXPUFC05_AGEPUFC06_MSTATPUFC07_GRADEPUFC08_CURSCHPUFC09_GRADTECHPUFC10_CONWRPUFC11_WORKPUFC12_JOBPUFC14_PROCCPUFC16_PKBPUFC17_NATEMPUFC18_PNWHRSPUFC19_PHOURSPUFC20_PWMOREPUFC21_PLADDWPUFC22_PFWRKPUFC23_PCLASSPUFC24_PBASISPUFC25_PBASICPUFC26_OJOBPUFC27_NJOBSPUFC28_THOURSPUFC29_WWM48HPUFC30_LOOKWPUFC31_FLWRKPUFC32_JOBSMPUFC33_WEEKSPUFC34_WYNOTPUFC35_LTLOOKWPUFC36_AVAILPUFC37_WILLINGPUFC38_PREVJOBPUFC40_POCCPUFC41_WQTRPUFC43_QKBPUFNEWEMPSTAT
0128280012405.2219420162171311149235025161011080241123110321011
1128280012388.828042016217132226123502519201204008222620081011
2128280012406.11944201621713331191350225192012080241121300250110341011
3128280022405.2219420162171411148232025161011040201123120331011
4128280022384.3556420162171422241235025191961120722220400115207221961
5128280022406.1194420162171433120135022515247208048222130020020481011
6128280022371.93314201621714432151320125222823
7128280032383.9971420162171411159235025161011040201123110321011
8128280032388.8280420162171422261235025153841080102222720101841
9128280032394.50084201621714362111250122
PUFREGPUFPRVPUFPRRCDPUFHHNUMPUFURB2K10PUFPWGTFINPUFSVYMOPUFSVYYRPUFPSUPUFRPLPUFHHSIZEPUFC01_LNOPUFC03_RELPUFC04_SEXPUFC05_AGEPUFC06_MSTATPUFC07_GRADEPUFC08_CURSCHPUFC09_GRADTECHPUFC10_CONWRPUFC11_WORKPUFC12_JOBPUFC14_PROCCPUFC16_PKBPUFC17_NATEMPUFC18_PNWHRSPUFC19_PHOURSPUFC20_PWMOREPUFC21_PLADDWPUFC22_PFWRKPUFC23_PCLASSPUFC24_PBASISPUFC25_PBASICPUFC26_OJOBPUFC27_NJOBSPUFC28_THOURSPUFC29_WWM48HPUFC30_LOOKWPUFC31_FLWRKPUFC32_JOBSMPUFC33_WEEKSPUFC34_WYNOTPUFC35_LTLOOKWPUFC36_AVAILPUFC37_WILLINGPUFC38_PREVJOBPUFC40_POCCPUFC41_WQTRPUFC43_QKBPUFNEWEMPSTAT
18085217595900408792207.73954201625817332111210122
18085317595900408792207.73954201625817432101220122
18085417595900408792207.7395420162581753291210122
18085517595900408792214.7598420162581763151000222
18085617595900408792214.759842016258177311
18085717595900408802239.434142016258151112923502511350108040222420401501
18085817595900408802189.8885420162581522229283025222823
18085917595900408802207.739542016258153324
18086017595900408802207.739542016258154322
18086117595900408802277.5219420162581558118135022511301104028222320281011